Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "124" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 60 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 58 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459875 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -1.284809 | 2.581776 | -0.043676 | 0.102437 | -0.218294 | -0.792964 | 0.705683 | 0.226270 | 0.0640 | 0.0735 | 0.0081 | nan | nan |
| 2459874 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459873 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.067411 | 3.306391 | -0.074201 | 0.061339 | -0.592956 | -1.141845 | 0.470528 | -0.117874 | 0.7183 | 0.6953 | 0.3719 | nan | nan |
| 2459872 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.737176 | 2.989907 | 0.640975 | -0.157528 | -1.005645 | -1.072268 | 0.665979 | -0.224246 | 0.0670 | 0.0653 | 0.0096 | nan | nan |
| 2459871 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -1.030052 | 2.462410 | 0.022447 | -0.114638 | -0.499302 | -1.363928 | 0.848186 | -0.043653 | 0.0772 | 0.0853 | 0.0099 | nan | nan |
| 2459870 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.665394 | 4.893456 | -0.170320 | -0.183514 | -0.159146 | -1.161498 | 1.184451 | 1.074626 | 0.7258 | 0.7052 | 0.3722 | nan | nan |
| 2459869 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.094401 | 3.236401 | -0.525141 | -0.201210 | -0.633665 | -0.727503 | 0.360725 | -0.203057 | 0.7327 | 0.7227 | 0.3680 | nan | nan |
| 2459868 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.477373 | 0.033521 | -0.259312 | 0.121616 | 0.146318 | -0.886837 | 0.834770 | 0.166906 | 0.7152 | 0.7030 | 0.3859 | nan | nan |
| 2459867 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.630978 | 0.117796 | 0.326382 | -0.264144 | -0.909565 | -1.298232 | 0.790110 | 0.321427 | 0.7260 | 0.7096 | 0.3874 | nan | nan |
| 2459866 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.634369 | 0.307030 | 0.364689 | 0.198413 | -0.713194 | -1.267688 | -0.138521 | -0.573629 | 0.7252 | 0.7100 | 0.3821 | nan | nan |
| 2459865 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.782375 | 0.087105 | 0.940228 | 0.070942 | -1.101027 | -0.792848 | -0.114330 | -0.550135 | 0.0699 | 0.0753 | 0.0095 | nan | nan |
| 2459864 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -2.354239 | 0.071277 | -0.188426 | -0.808029 | -0.764779 | -0.900645 | 0.913143 | 1.167375 | 0.7233 | 0.7062 | 0.3980 | nan | nan |
| 2459863 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.028996 | 0.294749 | -0.794544 | 0.146097 | -0.185576 | 0.558190 | 0.358961 | 0.110168 | 0.7179 | 0.6964 | 0.3908 | nan | nan |
| 2459862 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.179362 | 0.684748 | 0.326314 | -1.149922 | -0.958932 | -1.416862 | -0.003788 | 0.042932 | 0.6958 | 0.7158 | 0.4158 | nan | nan |
| 2459861 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.661830 | 0.322405 | -0.671236 | 0.342910 | 0.859612 | 0.797620 | 0.103585 | 0.463721 | 0.7254 | 0.6966 | 0.4061 | nan | nan |
| 2459860 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.785417 | 0.543314 | 0.149301 | -1.148190 | -0.423792 | -1.203652 | 0.154016 | -0.044749 | 0.7368 | 0.7063 | 0.3966 | nan | nan |
| 2459859 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.741762 | 0.255490 | -0.662845 | 0.328313 | 0.762462 | 0.269442 | 0.364862 | 0.503300 | 0.7409 | 0.7113 | 0.3932 | nan | nan |
| 2459858 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.589978 | 0.453590 | -0.737321 | 0.218667 | 1.083167 | 0.460961 | 0.623461 | 0.883217 | 0.7498 | 0.7163 | 0.4044 | 1.712033 | 1.486092 |
| 2459857 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 1.165751 | 1.021066 | 0.173916 | 0.184638 | -0.915226 | 1.078582 | 0.075536 | 5.493836 | 0.0295 | 0.0279 | 0.0020 | nan | nan |
| 2459856 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 3.43% | 0.00% | -0.878193 | 1.373387 | 0.182193 | -1.049791 | 0.071181 | -1.108517 | 0.004725 | 0.620455 | 0.7453 | 0.7267 | 0.3880 | 1.887864 | 1.535289 |
| 2459855 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 4.58% | 8.40% | -1.211728 | 1.608484 | 0.145927 | -1.315217 | -0.330233 | -0.800515 | -0.386425 | -0.351288 | 0.7334 | 0.7429 | 0.4149 | 1.808280 | 1.461813 |
| 2459854 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.031321 | 1.791340 | 4.728353 | 6.873543 | -0.165600 | -0.380617 | 1.503638 | 1.610202 | 0.7504 | 0.7335 | 0.4335 | 4.430657 | 2.860593 |
| 2459853 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.456600 | 1.537032 | 6.627300 | 9.294882 | 0.086622 | 0.452347 | 0.505088 | 0.137944 | 0.7709 | 0.6916 | 0.4234 | 4.089962 | 2.826913 |
| 2459852 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.568704 | -0.247563 | 6.532004 | 9.392688 | 1.233878 | 4.225068 | 4.759096 | 9.813927 | 0.8536 | 0.8409 | 0.2232 | 5.739842 | 5.024943 |
| 2459850 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.643261 | 0.885730 | 6.164129 | 8.817545 | -0.899184 | 0.312415 | -0.161455 | 0.204895 | 0.7753 | 0.7591 | 0.3441 | 3.784830 | 2.692302 |
| 2459849 | digital_ok | 100.00% | 0.00% | 0.00% | 100.00% | 100.00% | 0.00% | 0.700750 | -0.080154 | -0.473353 | 1.174974 | 6.331235 | 2.905898 | 5.651353 | 1.416677 | 0.4355 | 0.4300 | -0.2661 | 4.858603 | 5.015745 |
| 2459848 | digital_ok | 100.00% | 0.00% | 0.00% | 100.00% | 100.00% | 0.00% | 0.441717 | 1.589383 | 5.047894 | 18.678089 | 9.606273 | 10.103059 | 3.563682 | -0.575132 | 0.4091 | 0.4034 | -0.2895 | 4.212265 | 4.450253 |
| 2459847 | digital_ok | 100.00% | 0.00% | 0.00% | 100.00% | 100.00% | 0.00% | 1.298429 | 0.593716 | 5.188558 | 17.472707 | 7.973532 | 7.716917 | 1.308114 | -1.144715 | 0.3413 | 0.3398 | -0.3150 | 3.960347 | 4.639492 |
| 2459846 | digital_ok | 100.00% | 0.00% | 0.00% | 100.00% | 100.00% | 0.00% | 2.356642 | 6.703070 | 4.199548 | 14.776620 | 0.787953 | 7.037446 | 1.435753 | -0.773459 | 0.4227 | 0.4272 | -0.2454 | 1.075785 | 1.214983 |
| 2459845 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459844 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459843 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459842 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 2.158823 | 14.001487 | 0.344343 | 8.883996 | -1.402066 | -0.665016 | -2.118859 | 1.291831 | 0.7654 | 0.1129 | 0.5264 | 5.256278 | 1.333985 |
| 2459841 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459840 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 4.282887 | 3.854691 | 0.878401 | -1.311800 | 5.579513 | 6.494461 | 5.434546 | 6.081148 | 0.0366 | 0.0429 | 0.0131 | nan | nan |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | 0.313939 | 0.268526 | 14.679803 | 8.738001 | 4.857278 | 3.955389 | 7.227363 | 8.219771 | nan | nan | nan | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Shape | 2.581776 | -1.284809 | 2.581776 | -0.043676 | 0.102437 | -0.218294 | -0.792964 | 0.705683 | 0.226270 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Shape | 3.306391 | -1.067411 | 3.306391 | -0.074201 | 0.061339 | -0.592956 | -1.141845 | 0.470528 | -0.117874 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Shape | 2.989907 | 2.989907 | -0.737176 | -0.157528 | 0.640975 | -1.072268 | -1.005645 | -0.224246 | 0.665979 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Shape | 2.462410 | 2.462410 | -1.030052 | -0.114638 | 0.022447 | -1.363928 | -0.499302 | -0.043653 | 0.848186 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Shape | 4.893456 | -0.665394 | 4.893456 | -0.170320 | -0.183514 | -0.159146 | -1.161498 | 1.184451 | 1.074626 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Shape | 3.236401 | 0.094401 | 3.236401 | -0.525141 | -0.201210 | -0.633665 | -0.727503 | 0.360725 | -0.203057 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Temporal Discontinuties | 0.834770 | -0.477373 | 0.033521 | -0.259312 | 0.121616 | 0.146318 | -0.886837 | 0.834770 | 0.166906 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Temporal Discontinuties | 0.790110 | -0.630978 | 0.117796 | 0.326382 | -0.264144 | -0.909565 | -1.298232 | 0.790110 | 0.321427 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Power | 0.364689 | 0.307030 | -0.634369 | 0.198413 | 0.364689 | -1.267688 | -0.713194 | -0.573629 | -0.138521 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Power | 0.940228 | -0.782375 | 0.087105 | 0.940228 | 0.070942 | -1.101027 | -0.792848 | -0.114330 | -0.550135 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Temporal Discontinuties | 1.167375 | 0.071277 | -2.354239 | -0.808029 | -0.188426 | -0.900645 | -0.764779 | 1.167375 | 0.913143 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Temporal Variability | 0.558190 | -1.028996 | 0.294749 | -0.794544 | 0.146097 | -0.185576 | 0.558190 | 0.358961 | 0.110168 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Shape | 0.684748 | -1.179362 | 0.684748 | 0.326314 | -1.149922 | -0.958932 | -1.416862 | -0.003788 | 0.042932 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Temporal Variability | 0.859612 | 0.322405 | -0.661830 | 0.342910 | -0.671236 | 0.797620 | 0.859612 | 0.463721 | 0.103585 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Shape | 0.543314 | -0.785417 | 0.543314 | 0.149301 | -1.148190 | -0.423792 | -1.203652 | 0.154016 | -0.044749 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Temporal Variability | 0.762462 | -0.741762 | 0.255490 | -0.662845 | 0.328313 | 0.762462 | 0.269442 | 0.364862 | 0.503300 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Temporal Variability | 1.083167 | 0.453590 | -0.589978 | 0.218667 | -0.737321 | 0.460961 | 1.083167 | 0.883217 | 0.623461 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Temporal Discontinuties | 5.493836 | 1.021066 | 1.165751 | 0.184638 | 0.173916 | 1.078582 | -0.915226 | 5.493836 | 0.075536 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Shape | 1.373387 | -0.878193 | 1.373387 | 0.182193 | -1.049791 | 0.071181 | -1.108517 | 0.004725 | 0.620455 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Shape | 1.608484 | 1.608484 | -1.211728 | -1.315217 | 0.145927 | -0.800515 | -0.330233 | -0.351288 | -0.386425 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Power | 6.873543 | 1.791340 | -1.031321 | 6.873543 | 4.728353 | -0.380617 | -0.165600 | 1.610202 | 1.503638 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Power | 9.294882 | 1.537032 | -0.456600 | 9.294882 | 6.627300 | 0.452347 | 0.086622 | 0.137944 | 0.505088 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Temporal Discontinuties | 9.813927 | 0.568704 | -0.247563 | 6.532004 | 9.392688 | 1.233878 | 4.225068 | 4.759096 | 9.813927 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Power | 8.817545 | -0.643261 | 0.885730 | 6.164129 | 8.817545 | -0.899184 | 0.312415 | -0.161455 | 0.204895 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Temporal Variability | 6.331235 | 0.700750 | -0.080154 | -0.473353 | 1.174974 | 6.331235 | 2.905898 | 5.651353 | 1.416677 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Power | 18.678089 | 1.589383 | 0.441717 | 18.678089 | 5.047894 | 10.103059 | 9.606273 | -0.575132 | 3.563682 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Power | 17.472707 | 0.593716 | 1.298429 | 17.472707 | 5.188558 | 7.716917 | 7.973532 | -1.144715 | 1.308114 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Power | 14.776620 | 2.356642 | 6.703070 | 4.199548 | 14.776620 | 0.787953 | 7.037446 | 1.435753 | -0.773459 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Shape | 14.001487 | 2.158823 | 14.001487 | 0.344343 | 8.883996 | -1.402066 | -0.665016 | -2.118859 | 1.291831 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Temporal Variability | 6.494461 | 4.282887 | 3.854691 | 0.878401 | -1.311800 | 5.579513 | 6.494461 | 5.434546 | 6.081148 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Power | 14.679803 | 0.268526 | 0.313939 | 8.738001 | 14.679803 | 3.955389 | 4.857278 | 8.219771 | 7.227363 |